论文标题
用于评估自动驾驶系统的模型预测瞬时安全度量
Model Predictive Instantaneous Safety Metric for Evaluation of Automated Driving Systems
论文作者
论文摘要
具有自动驾驶系统（AD）的车辆在具有多代理相互作用的高维连续系统中运行。该连续系统具有由连续运动普通微分方程（差异驱动器）控制的各种类型的交通流（非均匀）。每个代理商都独立做出决定，可能导致与主体工具（SV）以及其他参与者（非合作性）发生冲突。典型的车辆安全评估程序，使用各种安全 - 关键方案并观察结果碰撞（或接近碰撞），不足以评估ADS在操作安全状态维持方面的性能。在本文中，我们介绍了一个模型预测性瞬时安全度量（MPRISM），该指标确定了SV的安全状态，考虑到给定的交通快照的最差安全场景。然后，该方法分析了SV在特定评估时间内与潜在碰撞的接近性。所述的度量标准在标准假设下的碰撞时间方面可促进安全性的理论保证。通过将解决方案作为一系列特定结构的一系列最小二次优化问题，该方法可用于实时安全评估应用。通过合成的示例和来自现实世界测试的案例证明了它的功能。
Vehicles with Automated Driving Systems (ADS) operate in a high-dimensional continuous system with multi-agent interactions. This continuous system features various types of traffic agents (non-homogeneous) governed by continuous-motion ordinary differential equations (differential-drive). Each agent makes decisions independently that may lead to conflicts with the subject vehicle (SV), as well as other participants (non-cooperative). A typical vehicle safety evaluation procedure that uses various safety-critical scenarios and observes resultant collisions (or near collisions), is not sufficient enough to evaluate the performance of the ADS in terms of operational safety status maintenance. In this paper, we introduce a Model Predictive Instantaneous Safety Metric (MPrISM), which determines the safety status of the SV, considering the worst-case safety scenario for a given traffic snapshot. The method then analyzes the SV's closeness to a potential collision within a certain evaluation time period. The described metric induces theoretical guarantees of safety in terms of the time to collision under standard assumptions. Through formulating the solution as a series of minimax quadratic optimization problems of a specific structure, the method is tractable for real-time safety evaluation applications. Its capabilities are demonstrated with synthesized examples and cases derived from real-world tests.
